Hand-held micro-raman based detection instrument and method of detection

ABSTRACT

A Raman spectroscopy based system and method for examination and interrogation provides a method for rapid and cost effective screening of various protein-based compounds such as bacteria, virus, drugs, and tissue abnormalities. A hand-held spectroscope includes a laser and optical train for generating a Raman-shifting sample signal, signal processing and identification algorithms for signal conditioning and target detection with combinations of ultra-high resolution micro-filters and an imaging detector array to provide specific analysis of target spectral peaks within discrete spectral bands associated with a target pathogen.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. application Ser. No. 14/910,459, filedFeb. 5, 2016, now U.S. Pat. No. 10,253,346, which is a national phaseentry of International Application No. PCT/US2014/050182, which claimsthe benefit of U.S. Provisional Application No. 61/893,095, filed onAug. 7, 2013, all of which are incorporated herein by reference.

FIELD

The present disclosure relates to a method and apparatus for rapidlydetecting and identifying protein-based compounds including bacteria,virus, drugs, or tissue abnormalities, and more particularly a portableRaman spectroscopy based spectroscope which is adaptable for examiningmucosal surfaces (nares, oral, ear), interrogating a wound site and/orinspection of a potentially contaminated object or surface forprotein-based compounds including MRSA or other pathogens. The devicecan be adapted to interrogate tissue specimens, stool, urine, serum orsecretions.

BACKGROUND

This section provides background information related to the presentdisclosure which is not necessarily prior art.

Methicillin resistance of Staphylococcus aureus (MRSA) is determined bythe mecA gene which is carried by a mobile genetic element, designatedstaphylococcal cassette chromosome mec (SCCmec). MecA encodes abeta-lactam-resistant penicillin-binding protein called PBP2a (orPBP2′). Beta-lactam antibiotics normally bind to PBPs in the cell walldisrupting the synthesis of the peptidoglycan layer which results inbacterium death. However, since the beta-lactam antibiotics cannot bindto PBP2a, synthesis of the peptidoglycan layer and the cell wallcontinues. While the mechanism responsible for mecA transfer is stillobscure, evidence supports horizontal transfer of the mecA gene betweendifferent staphylococcal species. Typically MRSA is diagnosed usingculture based methods.

The Clinical and Laboratory Standards Institute (CLSI) recommends thecefoxitin disk diffusion test supplemented with the latex agglutinationtest for PBP2a. Phenotypic expression of resistance can vary dependingon the growth conditions, as well as on the presence of subpopulationsof staphylococci that may coexist (susceptible and resistant) within aculture making susceptibility testing by standard microbiologicalmethods potentially problematic. In addition, culture takes time,usually 1 to 5 days. Faster techniques of MRSA screening by molecularmethods, such as Polymerase Chain Reaction (PCR), have been developed totest for the mecA gene that confers resistance to methicillin,oxacillin, nafcillin, and dicloxacillin and other similar antibiotics.Such techniques, while faster, still take hours and are sent out tolabs. In addition, commercially available molecular approaches (used forscreening) are unable to detect mecA-variants of MRSA.

Raman spectroscopy is a reagentless, non-destructive, technique that canprovide the unique spectral fingerprint of a chemical and/or moleculeallowing for target identification without sample preparation. With thistechnique, a sample is irradiated with a specific wavelength of lightwhereby a small component, approximately 1 in 10⁷ photons, isin-elastically scattered (at wavelengths shifted from the incidentradiation). The inelastic scattering of photons, due to molecularvibrations that change the molecule's polarizability, provide chemicaland structural information uniquely characteristic of the targetedsubstance. Raman Spectroscopy can be extremely useful in fullycharacterizing a material's composition, and allows for relatively fastidentification of unknown materials with the use of a Raman spectraldatabase. In addition, since Raman Spectroscopy is a non-contact andnon-destructive technique, it is well suited for in-situ, in-vitro andin-vivo analysis.

Raman Spectroscopy has high potential for screening of bacteria, virus,drugs, as well as tissue abnormalities since it: 1) is practical for alarge number of molecular species; 2) can provide rapid identification;and 3) can be used for both qualitative and quantitative analysis. Aportable or handheld micro Raman based detection instruments would beuseful to reliably and rapidly assess Staphylococcus aureus strains inwounds or nasal passages. Rapid assessment and typing would enabletracking the spread of such pathogens and could significantly decreasethe number of hospital-acquired infections and the associated costs intreatment thereof.

SUMMARY

This section provides a general summary of the disclosure, and is not acomprehensive disclosure of its full scope or all of its features.

A hand-held Raman spectroscopy based device system for mucosalexamination (nares, oral, ear) and wound interrogation provides a methodfor rapid and cost effective screening of bacteria, virus, drugs, andtissue abnormalities. The device is a nonintrusive automatednear-real-time-point-of-care detection system that can enable healthcareproviders to render better patient management and optimize clinicaloutcomes. The device includes a disposable tip element having dimensionsthat are small enough to fit into a small body cavity, such as thenostril. Three types of tip elements may be employed with thissystem—one for direct nasal interrogation, one with vacuum suction andfilter, and one with proximity optics for wound interrogation. The tipelement and the device enclose an assembly of optical components whichallow for Raman spectral measurement that can provide the uniquespectral fingerprint of a chemical and/or molecule allowing for targetidentification without sample preparation.

The device incorporates signal processing and identification algorithmsfor signal conditioning and target detection. Combinations of ultra-highresolution micro-filters in discrete regions (e.g., quadrants) of anarea on the imaging detector array provide specific analysis of targetspectral peaks. Each region or quadrant allows for discrete spectralband detection with each micro-filter providing specific wavenumberdetection for spectral analysis. Discrete Raman spectral bands thatdistinguish a targeted substance from background interference are usedto develop learning algorithms that serve as a basis for detection andtarget identification. By obtaining data at discrete spectral regionsinstead of over the entire spectral rage, acquisition time as well asspectral contributions from confounding background interference will bereduced or eliminated allowing for near-real-time assessment.

Methods for identification of pathogens in fluidic samples using Ramanspectroscopy also form a part of the present disclosure. These methodsshow use a limited number of discrete spectra peaks to sample keymolecular identifiers of a wide range of potential targets for specificpathogen detection. As a result, the apparatus and methods describedherein can be customized for a host of target materials and implementedin a relatively small, portable form factor. In essence, an adaptedsystem is provided, which can be readily modified to change target needsby means of a built-in learning algorithm. An exemplary learningalgorithm was developed under a United States Department of Defenseprogram for real-time pathogen detection in water as well as forreal-time identification of cancer cells from tissue samples and may beadapted into a detection protocol. After pre-processing is complete, aDiscriminant Function Analysis (DFA) is used to classify samples. DFApredicts membership in a group. The independent variables are thepredictors and the dependent variables are the groups, based on anassumption of multivariate normality. This resulting data is used tomodify the Raman-based spectral analysis executed in the hand helddevice.

Further areas of applicability will become apparent from the descriptionprovided herein. The description and specific examples in this summaryare intended for purposes of illustration only and are not intended tolimit the scope of the present disclosure.

DRAWINGS

The drawings described herein are for illustrative purposes only ofselected embodiments and not all possible implementations, and are notintended to limit the scope of the present disclosure.

FIG. 1 illustrates the mean spectra for various bacteria in terms of therelative intensity as a function of wavelength;

FIG. 2 illustrates the wave numbers or spectral bands ascertained withDFA distinguishing the tested bacteria;

FIG. 3 illustrates a cluster analysis of Raman spectra of bacteria;

FIG. 4 illustrates a comparison of mean spectra for inoculated nasalswab samples (light) and MRSA 1R (dark);

FIG. 5 illustrates the mean spectra of Staphylococcus with an expandedview of minimally obstructed spectral regions 600-740 (cm-1);

FIG. 6 illustrates the mean spectra of Staphylococcus with an expandedview of minimally obstructed spectral regions 1200-1300 (cm-1);

FIG. 7 illustrates a sample of the Raman spectra collected for threeinfluenza virus;

FIG. 8 illustrates spectral peaks for the three influenza virus at Ramanshifts between 2850 and 2950 cm⁻¹;

FIG. 9 illustrates spectral peaks for the three influenza virus at Ramanshifts between 700 and 1700 cm⁻¹;

FIG. 10 illustrates the spectra of immobilized influenza utilizingbackground subtraction techniques;

FIG. 11 illustrates spectra of an influenza virus which has beendeactivated using different deactivation procedures including UV,thermal and chemical treatment;

FIG. 12 illustrates an exemplary form factor of the hand heldmicro-Raman based detection instrument;

FIG. 13 illustrates the functional characteristics of the spectroscopeshown in FIG. 12;

FIG. 14 illustrates the components of the device shown in FIG. 12;

FIG. 15 a disposable end effector of the device for wound and/or nasalinterrogation;

FIG. 16 illustrates a disposable end effector of the device withfiltering function for vacuum suction application;

FIG. 17 is an end view of the end effector shown in FIG. 16;

FIGS. 18A and 18B illustrate simple examples of the beam expander shownin FIG. 14;

FIG. 19 illustrates the filter element shown in FIG. 14;

FIG. 20 illustrates another embodiment of a Raman probe and detectionsystem;

FIG. 21 illustrates the optical components of the Raman probe shown inFIG. 20;

FIG. 22A-C illustrates detailed aspects of the optical components shownin FIG. 20;

FIG. 23A-D illustrates a laser line filter, off-axis parabolic mirrorsystem and hexagonal conical lens for the Raman probe shown in FIG. 22;

FIG. 24 illustrates a portable form factor of the device shown in FIG.20; and

FIG. 25 is a flow chart illustrating the operating procedures carriedout during a pathogen detection procedure using the hand heldmicro-Raman based detection instrument.

Corresponding reference numerals indicate corresponding parts throughoutthe several views of the drawings.

DETAILED DESCRIPTION

Through preliminary studies explained in further detail below, thefeasibility of Raman Spectroscopy to assess various protein-basedcompounds including numerous pathogens using a laboratory RamanSpectrometer is demonstrated. For these studies the following pathogenswere evaluated in the absence of background interference:

-   MSSA-1S: Staphylococcus aureus subsp. aureus (ATCC® 6538 ™);-   MSSA-2S: Staphylococcus aureus subsp. aureus (ATCC® BAA1721 ™);-   MRSA-1R: Staphylococcus aureus (ATCC® BAA1683™);-   MRSA-2R: Staphylococcus aureus subsp. aureus (ATCC® 700787 ™);-   Corynebacterium sp. (ATCC® 6931 ™);-   Staphylococcus epidermidis (ATCC® 12228™);-   Influenza APR8/34 (H1N1);-   Influenza A/WSN/32 (H1N1); and-   Influenza A/Udorn/72 (H2N3).    Preliminary studies on additional pathogens (such as Bacillus    subtilis, E. coli K99, E. coli O111, E. coli O157, Enterobacter    amnigenus, Listeria monocytogenes, Pseudomonas aeruginosa, Rahnella    aquatilis, Salmonella Schottmueller, Salmonella Typhimurium,    Streptococcus pneumonia, Vibrio fluvialis, and Staphylococcus    epidermidus—ATCC#35984) demonstrate the feasibility of    identification by Raman Spectroscopy as further described herein.    These results provide a positive indication that a Raman spectral    database for a wide variety of protein-based compounds could be    developed with analysis protocols that allow for target    identification and classification. As such the system and method    described herein is not limited to examination, detection and    identification of MRSA and/or influenza but has a broader range of    application to examination, detection and identification of various    pathogens, toxins and other protein-based compounds.

The MRSA-2R strain of Staphylococcus aureus has reduced susceptibilityto vancomycin and was isolated from human blood from a patient withfatal bacteremia. The MSSA-2S strain of Staphylococcus aureus is ahyper-virulent community acquired methicillin-susceptible strainisolated in the United Kingdom. It is a complete genome sequencedstrain. The MRSA 1R strain of Staphylococcus aureus is Methicillinresistant and was isolated from a human abscess. It is confirmed tocarry the mec A gene with a SCCmec, or staphylococcal cassettechromosome mec type IV and PFGE type USA 400. The MSSA-1S strain ofStaphylococcus aureus is Methicillin sensitive and was isolated from ahuman lesion [ATCC].

Staphylococcus epidermidis and Corynebacterium are normal flora found inthe nose. Staphylococcus epidermidis with Corynebacteria predominantlycolonizes the upper respiratory tract, especially the nostrils. S.epidermidis accounts for 90%-100% of the staphylococci found in thenasal cavity when S. aureus is not present. However, when S. aureus ispresent, the amount of S. epidermidis drastically decreases. Mostspecies of Corynebacterium will not cause diseases in humans, however;Corynebacterium diptheriae NCTC 13129 is a strain that is highlyinfectious.

Samples were prepared from bacteria plated on tryptic soy agar plates. Asingle colony was picked and added to 5 mls of tryptic soy broth in a 10ml culture tube. The culture tube was place on a shaker in a 37Cincubator and incubated overnight. The next day an optical density (OD)was taken to verify the consistency of the growth conditions and toprovide a reference OD. The overnight culture was centrifuged at roomtemperature for 5 min @ 3000 rpms. After centrifuging the supernatantwas removed and the bacteria pellet was resuspended with 5 mls offiltered tap water. The bacteria were centrifuged as stated and thewashing process was repeated 2 more times. On the final wash the OD ofthe solution was measured and if the OD was greater than 1.05, water wasadded until an OD of 1+0.05 was obtained. 150 ul of the bacteriasuspension was then placed on a UV quartz substrate (Craic technologies)for Raman spectroscopy.

Raman spectra were recorded with an in-via Raman microscope (Renishaw®)equipped with a 1800 l/mm grating, a 50 mW 514.5 nm laser as theexcitation source at 100% laser power. The laser light was focused ontothe sample though a 63× dipping objective (Leica HCX PL APO 1.2NACorr/0.17 CS). The spectra were acquired over a spectral range of400-3200 cm-1 with 40 accumulations at an integration time of 10 s.

Prior to analysis, spectra were pre-processed using: (1) derivativesmoothing with a sliding window of 5; (2) range exclusion in the regionof 735-874 cm-1 and 1013-1116 cm-1 to eliminate quartz dominatedspectral regions; (3) background subtraction via a robust polynomial fitto remove spectral contributions due to fluorescence; and (4) vectornormalization to reduce bacteria concentration effects. The mean Ramanspectra are shown in FIG. 1.

A key to developing the Raman spectroscopy based detection device is thedevelopment of a Raman spectral database with analysis protocols thatallow for target identification and classification. As part of theanalysis protocol, Raman spectral bands that can distinguish a targetedsubstance from background interference are identified. These discretebands are used to develop learning algorithms that serve as a basis fordetection and identification. By obtaining data at discrete spectralregions instead of over the entire spectral range (600-1800 cm-1),acquisition time as well as spectral contributions of confoundingbackground interference can be reduced. The spectroscopic system withdiscrete spectral band identification for algorithms development isdetailed in embodiments of the device.

To identify discrete spectral bands of statistical significance, thepure spectra of bacteria in water were analyzed using discriminantfunction analysis, DFA (IBM SPSS Statistics 21). DFA builds a predictivemodel for group membership. The model is composed of discriminantfunctions that are based on linear combinations of predictor variables.Spectral data, that is to say wavenumber with associated Ramanintensity, corresponding to the following Raman peaks were utilized:600, 621, 643, 670, 725, 896, 935, 960, 1003, 1126, 1158, 1173, 1209,1249, 1297, 1320, 1338, 1362, 1375, 1397, 1420, 1449, 1480, 1578, 1584,1606, 1620, 1640, 1657 cm-1. Stepwise discriminant function analysis isused to reduce the number of variables (wavenumbers) to a subset ofinput into simultaneous discriminant analysis for classification. Oncethe model is finalized, cross validation is done based on the “leaveone-out” principle in which one individual is removed from the originalmatrix and the discriminant analysis is then performed from theremaining observations and used to classify the omitted individual.

A similar procedure and analysis can be used for other pathogens such asthe influenza virus described herein, as well as numerous otherprotein-based compounds.

The analysis for identifying the MRSA strains of bacteria is done basedupon 2-group classification scheme. First an investigation of theability of Raman spectroscopy to distinguish the Staphylococcus genusfrom other genus of bacteria is conducted. The Staphylococcus groupconsisted of MRSA 1R, MRSA 2R, MSSA 15, MSSA 2S, and S. epidermidis,while the non-staphylococcus group consisted of Bacillus subtilis, andCorynebacterium sp. The classification results show that 100% ofcross-validated grouped cases correctly classified with 100% of theStaphylococcus group and 100% of the non-Staphylococcus group correctlyclassifying. Five wavenumbers were utilized in the discriminant model;725 cm-1, 1158 cm-1, 1209 cm-1, 1420 cm-1, and 1450 cm-1, correspondingto vibrations of nucleic acids, proteins and lipids. Raman vibrationalband assignments are given in Table 1 shown below.

TABLE 1 Wavenumber cm⁻¹ Tentative Assignments from Literature Location620, 640 Amino acids (620 cm⁻¹ = phenylalanine, Protein 640 cm⁻¹ =tyrosine) 665-782 Nucleic acids (G,A,C,T,U) DNA/RNA 788 O—P—O sym str.DNA 810-820 Nucleic acids (C—O—P—O—C), A-type helix RNA 829, 852Tyrosine (buried, exposed) Protein 877-937 Protein [v(C—C)],carbohydrates [v(COC)], Carbohydrates, lipids protein, lipids 1003Phenylalanine v(C—C) ring breathing Protein 1030-1085 Protein[ v(C—N),v(C—C)], carbohydrate Protein, [v(C—O), v(C—C)], lipids carbohydrate,lipids 1095 DNA: PO₂- str (sym) DNA 1126 Protein [(v(C—N), v(C—C)],lipids[v(C—C)], Protein, lipids, carbohydrates [v(C—C), v(COC) glycosidelink] carbohydrates 1158 Protein [v(C—C)] Protein 1175 Aromatic aminoacids, Tyrosine [δ(C—H)], Protein 1230-1295 Amide III [v(C—N), N—H bend,C═O, O═C—N Protein, nucleic bend], 1230 cm⁻¹ = sat lipid acids, lipids1295, 1267 Lipids [δ(CH₂)] likely unsaturated Lipids 1320-1340 Nucleicacids (Guanine, Adenine), DNA/RNA, proteins, carbs (1340 cm⁻¹) proteins,carbohydrates 1336 Amino acids [C—H bend] Protein 1375 Nucleic acids(T,A,G) DNA 1420-1460 Lipids, carbohydrates, proteins [δ(C—H₂) Lipids,scissoring for each] carbohydrates, proteins 1483-1487 Nucleic acid (G,A), CH def. DNA 1518-1550 Amide II [N—H bend, v(C—N), v(C═C)] Protein1575-1578 Nucleic acids (G, A), ring stretching DNA 1585 Tryptophan,Phenylalanine Protein 1606 Phenylalanine, Tyr. Protein 1617 Tyrosine,Trp. Protein 1640 Water 1650-1680 Amide I [v(C═O), v(C—N). N—H bend],Protein, Lipid Lipid [C═C str] 1735 >C═O ester str. Lipids

Next, the feasibility of Raman spectroscopy to distinguish MRSA fromother Staphylococcus species and strains is determined. The analysiscontinues all the way to stain identification. The DFA classificationresults are provided in Table 2 shown below.

TABLE 2 Cross-validated Groups Results Wavenumbers for the DF GenusGroup 1: 100% of cross-validated 5 wavenumbers Staphylococcus groupedcases correctly Nucleic acids (725 cm⁻¹), Group 2: classified with 100%Protein (1158 ccm⁻¹, Bacillus Staphlocollus and 100% 1209 cm⁻¹),Lipids/protein and Cory (Cory and Bacillus) (1420 cm⁻¹), correctlyclassifying. Lipids/protein/carbohydrates (1450 cm⁻¹) MRSA from otherstaph Group 1: 90.7% of cross-validated 6 wavenumbers MRSA1R and groupedcases correctly Protein (621 cm⁻¹, MRSA 2R classified with 89.9% 1173cm⁻¹, 1338 cm⁻¹). Group 2: MRSA and 91.3% Protein, lipids, carbohydratesMSSA 1S, (MSSA and S. (1126 cm⁻¹), Lipid MSSA 2S, and epidermidis)correctly (1297 cm⁻¹), Lipids/protein S. epidermidis classifying. (1420cm⁻¹) MRSA 1R vs MRSA 2R Group 1: 100% of cross-validated 3 wavenumbersMRSA 1R grouped cases correctly Nucleic acids (1320 cm⁻¹, Group 2:classified with 100% 1584 cm⁻¹), MRSA 2R MRSA 1R and 100%Lipids/protein/carbohydrates MRSA 2R correctly (1375 cm⁻¹) classifying.MSSA from S. epidermis Group 1: 93.8% of cross-validated 5 wavenumbersMSSA 1S grouped cases correctly Protein (642 cm⁻¹, and MSSA 2Sclassified with 93.9% 1338, cm⁻¹). Protein, Group 2: S. Staphlocollusand 93.5% lipids, carbohydrates epidermidis (Cory and Bacillus) (1126cm⁻¹, 1450 cm⁻¹), correctly classifying. Nucleic acids (1578 cm⁻¹) MSSA1S from MSSA 2S Group 1: 100% of cross-validated 2 wavenumbers MSSA 1Sgrouped cases correctly Lipid/protein (1420 cm⁻¹), Group 2: classifiedwith 100% Lipids/protein/carbohydrates MSSA 2S MSSA1S and 100% (1450cm⁻¹) MSSA 2S correctly classifying.

The first column in Table 2 lists members of each group. The secondcolumn lists the cross-validated classification results. The thirdcolumn lists the specific wavenumbers utilized in the Discriminantfunction models which are shown accumulatively in FIG. 4. The results ofthis analysis indicate that MRSA can be separated from other bacteriadown to the strain level using a minimal number of Raman spectral bands.Further, methacillin sensitive strains of bacteria can also bedistinguished and identified.

Next, the detection of a target pathogen with a confounding backgroundis considered. For nasal analysis, background interference frompotential confounding factors is assessed. S. aureus most commonlycolonizes the anterior nares (the nostrils), although the respiratorytract, opened wounds, intravenous catheters, and urinary tract are alsopotential sites for infection. Since there are other bacteria andmaterial in the exterior nares, it is important to investigate theability to separate MRSA from other species of bacterium and confoundingfactors. Prominent nasal flora include Staphylococcus aureus,Staphylococcus epidermidis cells, Corynebacterium sp., andPropionibacterium sp. Nasal secretions may also include Mucin,Epithelial Cells and red blood cells.

For this work nasal swab samples are taken and inoculated with MRSA 1R,MRSA 2R, MSSA 1S or S. epidermidis. To determine if MRSA can bedistinguished in the presence of nasal secretions, a cluster analysis isperformed. The pure spectra, of Corynebacterium sp., Staphylococcusepidermidis, MSSA 2S and MRSA 2R in water as well as the spectra ofnasal swab samples inoculated with MRSA 2R are analyzed. FIG. 5 show theresults of a cluster analysis. The results show that nasal swab samplesinoculated with MRSA 2R are grouping with pure samples of MRSA 2Rindicating that Raman spectroscopy can be used to distinguish bacteriain the presence of confounding factors.

To determine regions of the Raman spectra that are not dominated bybackground interference, the pure spectra of MRSA 1R (in the absence ofbackground factors) was overlaid on the spectra of inoculated nasal swabsamples. FIG. 6 indicate that regions around 640-740, 1200-1265,1520-1560 and 1620-1700 cm-1 have minimal background contribution.

The spectra shown in FIG. 6 are pre-processed slightly different thanthose shown in previous figures. Due to the large intense peaks ofbackground components, spectra were pre-processed with (1) derivativesmoothing using a sliding window of 5; (2) background subtraction via arobust polynomial fit to remove spectral contributions due tofluorescence; and (3) normalization using the 1657 cm-1 peak as opposedto vector normalization.

The pure spectra, of bacteria in water, were re-analyzed with DFA usingdata only in the regions 640-740, 1200-1265 cm⁻¹. The ability of Ramanspectroscopy to distinguish the Staphylococcus genus from other bacteriagenus is shown in below. Five wavenumbers are utilized in thediscriminant model; 640 cm⁻¹, 672 cm⁻¹, 725 cm⁻¹, 1209 cm⁻¹, and 1225cm⁻¹, corresponding to vibrations of nucleic acids, and proteins.

TABLE 3 Groups Cross-validated Results Wavenumbers for the DF GenusGroup 1: 94.5% of cross-validated 5 wavenumbers Staphylococcus groupedcases correctly Protein (640 cm⁻¹, Group 2: classified with 95.8% 1209cm⁻¹), Nucleic acids Bacillus Staphylococcus and (672 cm⁻¹, 725 ccm⁻¹),and Cory 91.3% (Cory and Bacillus) edge of Amide III correctlyclassifying. peak (1225 cm⁻¹)

The classification results show that 94.5% of cross-validated groupedcases correctly classified with 95.8% of the Staphylococcus group, and91.3% of the non-Staphylococcus group (Cory and Bacillus) correctlyclassifying. These results indicate that the regions of 640-740 cm⁻¹,1200-1265 cm-1 have potential for bacteria identification. To test themodel further, nasal swab samples inoculated with MRSA 1R, MRSA 2R, MSSA15 or S. epidermidis were input into the analysis as unknowns. 100% ofthe cases correctly classified as staphylococcus. Further, the meanspectra of the Staphylococcus species and strains show clear distinctionin these regions as best seen in FIGS. 8 and 9.

The preliminary studies detailed above have shown a high confidencelevel that staph in general can be identified with 5 or less Ramanspectral regions. In one embodiment of the invention, the system isdesigned to acquire Raman measurements in the presence of confoundingfactors. Measurements will be made directly in the nasal vestibule. Thespectral regions of 640-740 cm-1, 1200-1265 cm-1, 1640-1740 cm-1 haveminimal spectral components due to confounding factors and show utilityfor this application. In another embodiment, the otoscope contains anasal aspirator allowing the sample to be drawn into the end effector ofthe otoscope through an internal filter. This filter in procedure willreduce the signal from background interference. The spectral bands forthis configuration are shown in FIG. 4.

An analysis for identifying influenza virus is done using a similarclassification scheme. Raman spectra have been obtained for severalpurified influenza viruses in phosphate buffer solution using hand heldmicro Raman spectrometer at an excitation wavelength of 514.5 nm fittedwith a fluidic probe as further described herein. An excitationwavelength of 514.5 nm, resulted in a significant fluorescence signalfrom the samples. However, Raman peaks associated with the targetviruses were sufficiently strong to be detectable from the backgroundfluorescent signal. A sample of the Raman spectra collected for threeinfluenza virus examined are shown in FIG. 7. The three strains ofinfluenza for which preliminary results are shown are as follows: A/PR/8and A/WSN/33 both being of the H1N1 serotype and A/Udorn/72 of the H3N2serotype.

The results confirm that Raman spectra can be obtained for influenzavirus. In addition to confirming the utility of Raman for theinvestigation of influenza viruses, the data collected confirms that anumber of Raman peaks exist for identification purposes. A comparison ofthe Raman spectra for A/PR/8 and A/WSN/33 shows a sufficient differencein the spectra, which provides distinguishing characteristics betweenviruses with the same serotype. The Raman spectra of all three virusesin FIG. 8 show a clear triplet of peaks at Raman shift between 2850 and2950 cm⁻¹. These peaks are clearly present on all influenza viruses thatwe have been examined to date.

This sample data clearly provides virus detection in general as comparedto spectrum from other biological entities. At Raman shifts ofapproximately 700 to 1700 cm⁻¹, as shown in FIG. 9, a large number ofdistinct peaks are observed for all virus samples. While many of thesepeaks are common to all viruses tested, a close examination shows thatthe relative heights of the specific peaks as well as shifts in theposition of some peaks differ with each virus strain. It is these peakratios and shifts that are utilized to distinguish the various strainsfrom one another. To increase the sensitivity and data characteristicsof influenza, FIG. 10 shows the spectra of immobilized influenzautilizing background subtraction techniques. The spectral bands clearlyidentify the distinguishing pleated sheet structure amide I group aswell as distinct carbon-carbon nucleic acids and other amide groups.These results clearly indicate influenza distinguishing abilities forRaman spectroscopy identification. The improvements in sensitivity andan increase in resolution in the described system will help inidentifying and distinguishing differences in this region.

The present disclosure further enables an analysis for distinguishing alive (active) virus from a dead (inactivated) virus. For example,results from sampling inactivated dried samples of A/PR/8 (H1N1)serotype influenza run at an excitation wavelength of 785nm revealeddifference in the Raman spectra of the virus based on the inactivationmethod utilized. The present disclosure has heretofore focused on activepathogen samples; however, preliminary results of testing the apparatusand methods described herein showed the Raman spectral data could beused to deactivation effects of the virus. In order to determine thedeactivation effects of the virus, a sample of A/PR/8 was deactivated bythree distinct methods: UV, heat, and chemical deactivation. When thesesamples were examined at an excitation wavelength of 785nm, cleardifference in the Raman spectra of the sample that was chemicallydeactivate were observed as can be seen in FIG. 11. This difference ismost obvious in the shift of the peak from 1080 to 1040 cm⁻¹, but canalso be seen in the minor shift of the peak located near 1340 cm⁻¹.Minor difference also exists between the UV and heat deactivatedinfluenza samples, but indicates the sensitivity to change in theanalysis. This information is useful for the identifying changes ormutations in the target virus. For example, FIG. 7 shows a significantportion of the mean Raman spectra of APR8/34 (H1N1), A/WSN/32 (H1N1),and A/Udorn/72 (H3N2) after preprocessing. Approximately 12 spectraaveraged of each pathogen were averaged and the classification resultsare reproduced in Table 4 below.

TABLE 4 Predicted Group Membership Virus Type WSN PR-8-34 UDORN TotalOrig- Count WSN 11 1 0 12 inal dimen- PR-8-34 0 8 0 8 sion 2 UDORN 0 012 12 % WSN 91.7 8.3 .0 100.0 dimen- PR-8-34 .0 100.0 .0 100.0 sion 2UDORN .0 .0 100.0 100.0

Example embodiments of a hand held micro Raman based detectioninstrument will now be described more fully with reference to FIGS.12-24 of the accompanying drawings. Example embodiments are provided sothat this disclosure will be thorough, and will fully convey the scopeof this disclosure to those who are skilled in the art. Specific detailsmay be set forth to provide a thorough understanding of embodiments ofthe present disclosure. It will be apparent to those skilled in the artthat specific details need not be employed, that example embodiments maybe embodied in many different forms and that neither should be construedto limit the scope of the disclosure. In some example embodiments,well-known processes, well-known structures, and well-known technologiesare not described in detail.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may include the pluralforms as well, unless the context clearly indicates otherwise. The terms“comprises,” “comprising,” “including,” and “having,” are inclusive andtherefore specify the presence of recited structure(s) or step(s); forexample, the stated features, integers, steps, operations, groupselements, and/or components, but do not preclude the presence oraddition of additional structure(s) or step(s) thereof. The methods,steps, processes, and operations described herein are not to beconstrued as necessarily requiring performance in the stated or anyparticular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional, alternative or equivalent steps may be employed.

When structure is referred to as being “on,” “engaged to,” “connectedto,” or “coupled to” other structure, it may be directly or indirectly(i.e., via intervening structure) on, engaged, connected or coupled tothe other structure. In contrast, when structure is referred to as being“directly on,” “directly engaged to,” “directly connected to,” or“directly coupled to” the other structure, there may be no interveningstructure present. Other words used to describe the relationship betweenelements should be interpreted in a like fashion (e.g., “between” versus“directly between,” “adjacent” versus “directly adjacent”). As usedherein, the term “and/or” includes any and all combinations of one ormore of the associated referenced items.

Terms of degree (e.g., first, second, third) which are used herein todescribe various structure or steps are not intended to be limiting.These terms are used to distinguish one structure or step from otherstructure or steps, and do not imply a sequence or order unless clearlyindicated by the context of their usage. Thus, a first structure or stepsimilarly may be termed a second structure or step without departingfrom the teachings of the example embodiments. Likewise, spatiallyrelative terms (e.g., “inner,” “outer,” “beneath,” “below,” “lower,”“above,” “upper”) which are used herein to describe the relative specialrelationship of one structure or step to other structure or step(s) mayencompass orientations of the device or its operation that are differentthan depicted in the figures. For example, if a figure is turned over,structure described as “below” or “beneath” other structure would thenbe oriented “above” the other structure without materially affecting itsspecial relationship or operation. The structure may be otherwiseoriented (e.g. rotated 90 degrees or at other orientations) and thespatially relative descriptors used herein interpreted accordingly.

With reference now to FIG. 12, an exemplary form factor (e.g, Tele-View®Wireless Otoscope by Advanced Monitors Corp.) for the hand held Ramanspectroscopy based system 10 is shown and which includes a miniaturelaser package and optics. The hand held system may be configured as anotoscope for testing in ears, nose and throat, as an ophthalmoscope fortesting in the eyes or, more generally, as a hand held spectroscope fortesting wounds sites, food or inanimate surfaces. Functional componentsof the system components include a hand held form factor housing 12 and,a disposable interrogation tip or end effector 14 that is used for nasalinterrogation. The system can be used with three types of endeffectors—one for direct nasal interrogation, one with vacuum suctionand filter, and one with proximity optics for wound interrogation. Othercomponents not shown in FIG. 12 but illustrated and describedhereinafter include optical sampling head having a hybrid micro mirror,an integrated micro CCD or CMOS imager with ultra-high resolution narrowrange spatially graded filter (takes the place of a large delicatespectrometer), signal processing and identification algorithms forsignal conditioning and target detection.

A key to developing a hand-held Raman spectroscopy based device is thedevelopment of analysis protocols that allow for target identificationand classification. Spectral analyses from discrete Raman bands thatdistinguish a targeted substance from background interference form thebasis of this development. These discrete bands are used to developlearning algorithms that serve as a basis for detection andidentification. A diagram of the functionality of hand-held device isschematically illustrated in FIG. 13 to include a probe front end 16, aset of micro-graded filters 18 and an imager 20. The set of micro-gradedfilters are designed for filtering at the discrete band, e.g. filters18A-18D. As presently preferred, Filter 18A is a micro-graded filtercovering spectral band 640-740 cm⁻¹ (˜3 nm band), filter 18B is amicro-graded filter covering spectral band 1200-1260 cm⁻¹ (˜2 nm band),filter 18C is a micro-graded filter covering spectral band 1520-1560cm⁻¹ (˜1.3 nm band), and filter 18D is a micro-graded filter coveringspectral band 1620-1750 cm⁻¹ (˜4.4 nm band). It should be noted for anexcitation beam at 532 nm, filter 18A filters a band in the range of550.57-553.80 nm, filter 18B filters a band in the range of568.28-570.22 nm, filter 18C filters a band in the range of578.80-580.18 nm, and filter 18D filters a band in the range of582.17-586.61 nm. The imager 20 may be a CCD, a CMOS or other similardigital imaging devices.

This point-of-care (POC) diagnostic technology is relatively low costand demonstrates feasibility for use in the resource-limited settingsand triage settings to non-clinical utilities. The device allows forsample collection (with disposable nasal end effector on the device),processing and result read-out in the same area, without the need tosend samples to a central collection point for processing or testing. Itrequires no sample manipulation and provides safe-containment ofbio-hazardous material with routine disposal of the disposable tip.Output is provided in a visual format, without ambiguity, and includes afull process negative and internal positive control. Read-outs areavailable as inputs into medical management protocols. The system mayalso include an integrated barcoding system as a way of connecting asample taken perhaps hours earlier to the individual who provided thatsample.

As noted above, the device is designed for operation in non-idealconditions, which are expected for a field or point of care deployableinstrument. This includes an ability to operate under temperatureextremes between 0 and 45 degree Celsius. If the design is such thatversion capable of operating from −25 to +50 degrees Celsius could beproduced, but may require heaters to prevent freezing that could impactbattery life. The device is also designed to be water and dirt resistantto allow the devices to operate under non-ideal conditions. Only thedisposable end effector is exposed to the patient, thus no sterilizationor cleaning of the device will be required between uses. The exposedsurfaces of the device may be fabricated with antimicrobial orbacterium-resistant material.

The device, as an option, may also utilize existing bar code braceletsif already assigned at the point-of-care facility or site. The deviceincludes a small low-power processor to operate the device, collect andanalyze data, and store results. The device includes a USB controller toallow for the downloading of data from the POC device's internal storageto external devices, as well as real time display. The device on anauxiliary monitor may also include standard wireless/cellular cards ifdesired. The device utilizes an externally accessible, readilyswappable, rechargeable battery pack for power.

A schematic representation of the components of the hand-held Ramanspectroscopy based device 10 is shown in FIG. 14. In this example,radiation from laser 22 is directed through a laser line filter 24,which transmits laser light while suppressing ambient light, to a 45°beam splitter 26. The beam splitter 26 reflects the laser light throughthe disposable end effector 14 to the samples where it interacts withthe sample producing a Raman shifted signal. Light is collected from thesample at a 180-degree geometry and is transmitted through the beamsplitter 26 and laser blocking filter 28. The laser blocking filterfurther prevents undesired laser light from reaching the detector 30.

The Raman shifted signal then impinges upon a beam expander 32 (forexample a simple beam expander 32A, 32B as shown in FIGS. 18A and 18B,respectively) that increase the diameter of a collimated input beam to alarger collimated output beam. The particular configuration and shape ofthe beam expander optics may be changed, such as off axis parabolicreflection, to make the beam expander more efficient and easily packagedwithin the device. The optical signals of the output beam are convertedto electrical signals by an imager 30 with ultra-high resolution narrowrange spatially graded filter 34 for processing. Filter 34 preferablyincludes a set of micro-graded filters 34A-34D as described in referenceto FIG. 13.

The disposable end effector 14 schematically illustrated in FIG. 15 isan attachment that interacts with the patient either inserted into thenasal passage or in proximity to a wound or infection situs. For directnasal or wound interrogation, a lens 36 is integrated at tip of the endeffector to allow laser light be focused onto the specimens and Ramanscattered light to be collected. As shown in FIG. 16, a modified endeffector 14′ is used when sample filtration is required. The endeffector 14′ will connect to a vacuum source 38 allowing the sample tobe drawn into the end effector body through an internal filter 40. Inone embodiment, the device may be fitted with a small vacuum pump whichfunctions as the vacuum source 38 removes gas molecules (air) from asealed volume, denoted by the dashed line in FIG. 14, in order to leavebehind a partial vacuum. The vacuum will draw the sample into the endeffector 14′. Raman measurement takes place at an optical window 42fabricated out of an optically transparent material such as quartz. Theoptical window 42 is located concentrically within a mesh 44 and a seal46 formed on an end of the end effector 14′ opposite the filter 40.Filtering the sample will reduce the signal from background interferenceby trapping large debris allowing bacteria or virus to pass through formeasurement.

The system is configured to deliver and collect light from the sampleusing an open beam path. Lens tubes 24, 38 are utilized to isolate theoptical path and reduce stray light.

The end effectors 14, 14′ shown are disposable specula that detachablyconnects to the head 48 of the device 10 with, for example a twist lockconnection to allow for precise optical alignment and ease of endeffector (tip) removal. The end effector connectors may be equipped withor without a focusing lens. For vacuum suction application, theconnector will house a lens 36 to allow laser light be focused onto thesample and Raman scattered light to be collected. For wound and directnasal interrogation, the lens will be absent. The specula is designed asa single use component that is detached from the device head 48 anddisposed in accordance with medical waste disposal procedures.

For this system, the incident beam and collected signal light share acommon path such that a 45° beam splitter 26 is used to reflect thelaser light through the optics to the sample while efficientlytransmitting the returning Raman-shifted signal light. A laser-blockingfilter 24 at normal incidence is used ahead of the dispersion element 26to completely block the undesired laser light. The diameter of acollimated input beam is increased with a beam expander 32 to a largercollimated output beam. With reference to FIGS. 13, 19A and 19B, a setof ultra-high resolution micro-filter quadrants 34A-34D are arranged infront of the imaging detector 30 and provide specific wavenumber orspectral band filtering by the discrete waveband analysis. Each quadrant34A-34D allows for discrete spectral band detection with eachmicro-filter providing specific wavenumber detection for spectralanalysis. The quadrants 34A-34D may be arranged symmetrically about thex and y axes as shown in FIG. 19A, or arranged in vertical bands asshown in FIG. 19B. The image sensor 30 converts the optical signals,into electrical signals. The imaging sensor 30 can be an integrated CCDor CMOS or the like.

The unique micro optical filters provide a narrow range of spatiallygraded filter, which span the narrow spectral region covering a specificRaman Spectral peak or narrow region of closely neighboring peaks.Commercial graded filters do not have sufficient resolution to achieve 1cm-1 spatial resolution. The spectral wavelength is transformed to animaging array position/intensity reading that provides a reconstructionof the spectral peaks of interest. The method of fabrication is a gradedIndium Aluminum Nitride (InAlN) alloy that can provide spectralfiltering by band gap engineering at any region between 1 eV and 6 eVband gap or 1240 nm to 206 nm. A hollow cathode based low energy plasmadeposition is used to deposit the nitride alloy. Deposition iscontrolled by a sliding substrate window coordinated with a change inIndium deposition rate creating the graded optical coating.

A narrow line width laser 22 packaged in a module with integral driveelectronics is used for Raman excitation. The wavelength and laser poweris chosen based upon the application. The laser is able to be used as anopen beam source or be coupled to an optical waveguide.

The spectrometer subsystem includes an electronic sub-system as well asan internal lithium-ion battery pack 52 to provide power to the systemand allow for field-portable use. The system 10 is powered from eitherits internal battery pack or via an external charger/power adapter. Thedevice 10 may have a provision for monitoring battery life and chargestatus. The device 10 may be designed with a USB controller (not shown)to allow for the downloading of data from the internal storage of thepoint of care (POC) device to external devices as well as a real timedisplay (not shown). In the form of a compact LCD panel. The device mayalso be built to accommodate standard wireless/cellular communication ifdesired. The spectrometer electronic subsystem 50 utilizes a dedicatedmicro-controller to read the spectrum measured with the imaging sensor30, performs the basic processing of the image data, and transmits thatinformation to a display, PC or other similar interface. As previouslynoted, the device 10 may be fitted with a small vacuum 38 for pump towork in conjunction with the disposable end effector 14′ with filter forvacuum suction application.

In another embodiment, an device 110 is designed as a Raman probe withoptic connection to a portable detection system 112 as shown in FIGS. 20and 24. The device 110 is designed to deliver laser light to the sampleand collect Raman scatter. To accomplish this, the device 110 isconfigured with waveguides, lenses, and filters that function totransmit the Raman scatter from the sample to the detection system forspectral analysis in a manner similar to that described with respect todevice 10.

The detection system 112 is a portable unit approximately 24 cm×10 cm×3cm in size (6″×4″×1″). Key components include a laser 114 opticallycoupled to the device 110 for Raman excitation, and a spectrographsubunit 114 optically coupled to the device 110 for the measurement ofRaman radiation intensity as a function of wavelength. A spectrographsubunit 116 indicated by the dashed box in FIG. 20 can be configured as,but is not limited to: a grating spectrometer, a prism spectrometer, oran interferometer. The detection system 112 will also incorporate amicro controller 118 for signal processing and identification algorithmsfor signal conditioning and target detection, as well as support a userfriendly graphical display that acts as the human-machine interface. Acolor LCD display will have sufficient resolution to display useinstructions, as well as test results in text output for go/no-goclassification, and to graphically display a spectra. A simple menustructure with large pushbutton icons make operation of the devicestraight forward and user friendly.

As shown in FIGS. 20 and 24 the spectroscope subunit 116 is configuredas a Czerny-Turner spectrometer. Radiation from laser 114 is directedthrough a flexible optical waveguide (fiber) 120 to the device 110 andis transmitted through a laser line filter 122 and disposable endeffector 124 to the samples. The light interacts with the sampleproducing a Raman shifted signal which is collected at 180-degreegeometry. The collected light is transmitted thought a laser blockingfilter 122 and coupled into a flexible optical waveguide (fiber) 126.The laser blocking filter 122 prevents undesired laser light fromreaching the detector. The Raman shifted signal is directed through theoptical waveguide (fibers) 126 to the spectroscope subunit 116 of thedetection system 112. Light entering the subunit 116 is reflected off ofthe collimating mirror 128 and is directed onto the diffraction grating130 which separates incident polychromatic light into constituentwavelength components. The diffracted light is directed to a focusingmirror 132 onto a detector 134 which converts optical to electricalsignals for processing.

As presently preferred, the disposable end effector 124 is a disposablespecula that interacts with the patient either inserted into the nasalpassage or in proximity to a wound or infection sight. The end effectordesign is similar to that described in FIGS. 15-17.

Further details of the optical train for the device 110 are illustratedin FIGS. 21-23D. Light from the laser 114 is coupled into the excitationfibers 120 e of the probe as shown in FIG. 19. As best seen in FIG. 22A,the excitation fibers 120 e form part of the fiber bundle 120 which areconcentrically arranged around the collection fiber 120 c. In apreferred embodiment, the collection fiber 120 c has a diameterapproximately four times larger than the diameter of the excitationfiber 120 e. A high rejection filter (laser line filter) 122A at theoutput of these fibers is used to remove Raman bands arising from thesilica core, thus allowing only the laser light to be transmitted to thesample. Hollow core Photonic crystal fibers are used as excitationfibers in order to reduce/eliminate the need for filtering.

Off axis parabolic mirrors 136, located beneath the excitation fibers120 collimate and direct the beams to a 45 degree cone lens 138. FIG.22C illustrate an excitation beam transmitted from the excitation fibers120 e and impinging on the face of the cone lens 138. As presentlypreferred, the height of the cone lens 138 is approximate twice thediameter of the excitation fiber 120 e as best seen in FIG. 22B. Thislens 138 has dielectric coated faces that allow the laser light to bereflected and the Raman scatter to be transmitted. In particular, theoutside surface of the lens 138 is coated with a dielectric to reflectlaser light and pass Stokes scattered light. The reflected laser lightis directed toward the sample surface and focused with a convex lens.When the lens is absent, collimated light is output from the probe.Light scattered from a sample is collected 180 degrees relative to thedirection of the laser beam. It is directed through the cone lens 138which allows only the Raman scattered light to be coupled into thecollection fiber 126.

Two key elements of this design are the off axis parabolic mirror 136system and the 45 degree cone lens 138. As best seen in FIG. 23B, theoff axis parabolic mirror is an annular or doughnut shaped optic thathas eight conic depressions or dimples 140 on its surface. Each of theeight dimples 140 forms a 90 degree parabolic mirror with its focalpoint at a designated excitation fiber 120 e. As shown in FIGS. 21, 22B,22C, 23C and 23D, the cone lens 138 is a hollow hexagonal opticalelement whose faces are at a 45 degree angle. The lens 138 has adielectric coating enabling it to act as a long pass filter (reflectinglaser light and transmitting the Raman scatter).

Other features incorporation into the system includes: a strain reliefboot 142 which provide strain relief to fiber cables, and exhibit a highdegree of flexibility. A first connector 144 secures the excitationfiber (waveguide) of the device 110 to the laser 114. A second connector146 secures the Raman collection fiber (waveguide) of the device 110 tothe spectrograph subunit 116. A narrow line width laser 114 is packagedin a module with integral drive electronics for Raman excitation. Thewavelength and laser power is selected based upon the application andtarget identification. The laser is coupled to an optical fiber orwaveguide with use of a third connector.

The second fiber connector 146 secures the input fiber 126 (orwaveguide) to the spectrograph subunit 116. Light from the input fiber(or waveguide) enters the detection system through this connector.Behind the connector, a slit (not shown) having a dark piece of materialcontaining a rectangular aperture may be utilized. The collimatingmirror 128 focuses light entering the spectrometer portion of thedetection system towards the grating 130. Diffraction grating 130diffracts light from the collimating mirror 128 and directs thediffracted light onto the focusing mirror 132. The dispersive element130 separates incident polychromatic light into constituent wavelengthcomponents and can be a grating or prism or a like. Focusing mirror 132receives light reflected from the grating 130 and focuses the light ontothe CCD Detector 134. CCD detector 134 collects the light received fromthe focusing mirror 132 and converts the optical signal to a digitalsignal. Each pixel on the CCD Detector corresponds to the wavelength oflight that strikes it, creating a digital response signal.

As noted above, the detection system 112 includes an internallithium-ion battery pack (not shown) to provide power to the system andallow for field-portable use. The system can be run from either itsinternal battery pack or via an external charger/power adapter. Thedevice 112 will have a provision for monitoring battery life and chargestatus. The detection system 112 may include an electronic sub-systemwhich includes a PC-based processor 148, spectrometer 116, vacuum pumpand valve controller (not shown), pressure sensors (not shown), andinterlocks. PC-based processor is used to perform all of the computationand coupled to an LCD display 150 with a touch screen and/or perimeterfunction buttons 152 to handle menu selection. The spectrometersubsystem 112 utilizes a dedicated micro-controller 118 to read the CCDarray, perform basic processing on the image data, then transmit thatinformation to the PC using a USB or other similar interface.

With reference now to FIG. 25, a flow chart 210 illustrating thedetection process is provided. In particular, a hand-held Ramanspectroscopic device as described above is operated to transmit acoherent light beam from the excitation laser onto a sample. The imagingsensor detects radiation from the filtered Raman-shifted sample signal(block 212) and generates image data representative thereof (block 214).The image data is then analyzed (block 216) and the spectral features atdiscrete spectral bands are examined to detect the presence of a targetpathogen (block 218). If no target pathogenic features are found, thedevice displays and/or reports a negative result for the presence of thetarget pathogen (block 220).

If target pathogenic features are found, these features are comparedwith baseline Raman spectra (block 222). Algorithms and classificationcoefficients are computed based on the baseline spectra (block 224).Typing of the target pathogenic features is done in a hierarchicalapproach and classification is assigned as the comparison moves down thehierarchy (block 226). If a positive database match is identified, thedevice displays and/or reports a positive result for the presence of thetarget pathogen (block 228). If a positive database match is notidentified, the probability of the target pathogen's identity ormembership within a particular group of interest may be computed anddisplayed or reported (block 230).

A robust portable Raman spectroscopy based system as detailed above hasmany anticipated benefits. The nonintrusive, nondestructive techniquefor nasal examination and wound interrogation provides rapid and costeffective screening of a wide range of protein-based compounds includingbacteria, virus, drugs, and tissue abnormalities. The method requireslittle or no sample preparation, reducing the need for storage ofconsumables. In addition, the ease of use and non-contact sampling makethe device a valuable tool for point of care investigations.

The device is a reagentless automated near real time point of caredetection system that can enable healthcare providers to render betterpatient management and optimize clinical outcomes. Since the sensor canbe developed to analyze bacteria, virus, drugs, and tissue, it can bepromoted to a variety of market segments that include: primary carephysicians, and drug stores.

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure. Individual elements or featuresof a particular embodiment are generally not limited to that particularembodiment, but, where applicable, are interchangeable and can be usedin a selected embodiment, even if not specifically shown or described.The same may also be varied in many ways. Such variations are not to beregarded as a departure from the disclosure, and all such modificationsare intended to be included within the scope of the disclosure.

1-20. (canceled)
 21. A Raman spectroscopic system comprising: anexcitation light source to radiate coherent light onto a sample; acollector to collect a Raman signal from the sample; a beam collimatorto collimate the collected Raman signal and generate a collimated Ramansignal; an optical filter to filter the collimated Raman signal, whereinthe optical filter comprises a plurality of micro-filters arrangedco-planarly and spatially adjacent to each other, and each of themicro-filters is configured to transmit a different spectral band of thecollimated Raman signal; and an imaging sensor array comprising aplurality of detection areas to separately detect each of a plurality ofdifferent spectral bands transmitted from each of the micro-filters. 22.The system of claim 21, wherein the different spectral bands comprise atleast one of 640-740 cm⁻¹, 1040-1080 cm⁻¹, 1200-1265 cm⁻¹, 1320-1340⁻¹,1520-1560 cm ⁻¹, 1620-1750 cm⁻¹, and 2850-2950 cm⁻¹.
 23. The system ofclaim 22, wherein the different spectral bands comprise at least two of640-740 cm⁻¹, 1200-1265 cm⁻¹, 1520-1560 cm⁻¹, and 1620-1750 cm⁻¹. 24.The system of claim 21, wherein the micro-filters each have a spectralresolution of 1 cm⁻¹.
 25. The system of claim 21, wherein themicro-filters are micro-graded filters.
 26. The system of claim 21,wherein the micro-filters each comprise a graded Indium Aluminum Nitride(InAlN) alloy coating.
 27. The system of claim 21, further comprising adispersive element to disperse the collimated Raman signal and directsthe dispersed collimated Raman signal to the optical filter.
 28. Amethod for distinguishing an active pathogen from an inactivatedpathogen using Raman based spectroscopic analysis, the methodcomprising: radiating coherent light onto a sample; collecting a Ramansignal from the sample; analyzing the Raman signal for the presence of apathogen; and analyzing the Raman signal for a Raman peak spectral shiftindicative of a change in activity of the pathogen.
 29. The method ofclaim 28, further comprising collimating the Raman signal, dispersingthe collimated Raman signal, and directing the dispersed Raman signal toan optical filter.
 30. The method of claim 28, further comprisinganalyzing the Raman signal for the Raman peak spectral shift that is ina spectral band selected from 640-740 cm⁻¹, 1040-1080 cm⁻¹,1200-1265cm⁻¹, 1320-1340⁻¹, 1520-1560 cm⁻¹, 1620-1750 cm⁻¹, and 2850-2950 cm⁻¹.31. The method of claim 28, further comprising simultaneously filtering,by the optical filter, the dispersed Raman signal in a plurality ofdifferent spectral bands.
 32. The method of claim 31, wherein thedifferent spectral bands comprise at least one of 640-740 cm⁻¹,1040-1080 cm⁻¹, 1200-1265 cm⁻¹, 1320-1340⁻¹, 1520-1560 cm⁻¹, 1620-1750cm⁻¹, and 2850-2950 cm⁻¹.
 33. The method of claim 32, wherein thedifferent spectral bands comprise at least two of 640-740 cm⁻¹,1200-1265 cm⁻¹, 1520-1560 cm⁻¹, and 1620-1750 cm⁻¹.
 34. The method ofclaim 31, further comprising detecting the filtered Raman signal by animaging sensor array, the imaging sensor array comprising a plurality ofdetection areas to separately detect each of a plurality of differentspectral bands of the filtered Raman signal.
 35. The method of claim 29,wherein the optical filter comprises a plurality of micro-filtersarranged co-planarly and spatially adjacent to each other, and each ofthe micro-filters is configured to transmit a different spectral band ofthe dispersed Raman signal;
 36. A method of making a graded opticalfilter, comprising: depositing an Indium Aluminum Nitride (InAlN) alloycoating on a substrate by hollow cathode-based low energy plasmadeposition; sliding the substrate during the deposition; and changingIndium depositing rate to coordinate with sliding of the substrate tocreate a graded InAIN coating having a band gap between 1 eV and 6 eV.37. The method of claim 36, wherein the graded optical filter comprisesa plurality of micro-graded filters each having a different spectralband.
 38. The method of claim 37, wherein the different spectral bandscomprise at least one of 640-740 cm⁻¹, 1040-1080 cm⁻¹, 1200-1265 cm⁻¹,1320-1340⁻¹, 1520-1560 cm⁻¹, 1620-1750 cm⁻¹, and 2850-2950 cm⁻¹.
 39. Thesystem of claim 32, wherein the different spectral bands comprise atleast two of 640-740 cm⁻¹, 1200-1265 cm⁻¹, 1520-1560 cm⁻¹, and 1620-1750cm⁻¹.
 40. The method of claim 37, wherein the micro-filters each have aspectral resolution of 1 cm⁻¹.